1. Field of the Invention
The present invention relates to an image processing method and an apparatus thereof. More particularly, the present invention relates to an image processing method for noise reduction, and an apparatus thereof.
2. Description of Related Art
With development of multi-media technique, demand of high quality images by people is increased. However, a main factor that influences the quality of an image is noises generated during image capturing, signal conversion and signal transmission. Therefore, an image processing technique has to be applied to eliminate the noises that influence visual feelings of human eyes. Commonly used image processing methods for noise reduction include a spatial noise-reduction process and a temporal noise-reduction process.
The spatial noise-reduction process applies a filter having local window blocks to perform spatial filtering process to pixels of a current image, so as to smooth and soften the image, and accordingly a visual perception of the noises by human eyes can be reduced. However, such method generally leads to an image blur effect, which may influence presenting of image details, for example, edges and textures.
The temporal noise-reduction process references information of a previous image to perform temporal filtering process to the pixels of the current image. Since the current image is highly related to the previous image, compared to the spatial noise-reduction process, the temporal noise-reduction process can maintain and reserve details of the image. However, when the temporal filtering process is performed to a moving object within the image, a motion blur phenomenon is liable to be generated, which can cause an uncomfortable feeling of human eyes. Therefore, a motion estimation algorithm is provided to estimate a motion vector trend of the whole image, so as to adjust an intensity of the temporal filtering for eliminating the motion blur. However, the motion estimation algorithm is complicated and requires a large amount of calculation, and it is hard to achieve a real-time processing of the current image when the motion estimation algorithm is implemented by hardware. Moreover, degree of image distortion that can be born by human eyes is varied according to environmental light source and image variations, so that during the noise-reduction process, besides the motion blur is required to be avoided, influences of the environmental light source and image characteristics are also taken into consideration.